The optoelectronic sensor acquires an image of a monitoring area in order to detect and evaluate objects therein. In principle, any conventional camera suffices, but the following specification mainly deals with 3D sensors. These include 3D cameras of various technologies, such as stereoscopy, triangulation, light time of flight, and with evaluation of variations of passive two-dimensional patterns or projected illumination patterns. 3D sensors, in contrast to a conventional camera, take images which include a distance value in their pixels. The depth-resolved or three-dimensional image data are also referred to as depth maps. Furthermore, laser scanners scanning in two or all three directions are known which also detect three-dimensional image data via the respective scanning angle and the measured distance. The higher device and evaluations costs for generating three-dimensional image data as compared to two-dimensional image acquisition is justified in many applications by the additional information.
One specific field of application is safety technology, where the sensor is used as a protective device to detect a state within the monitored area classified as critical, which leads to a warning or a shutdown of a source of danger. Safety technology has specific requirements because health or even life of persons depends on the correct operation of the sensor.
In particular in the field of safety technology, but in principle in any visual monitoring, image defects are problematic because the sensor is partially blind. Therefore, great efforts are made to obtain a high-quality image as a starting point. A simple example of these image defects is a local glare of a gloss reflection.
For stereo cameras, an active illumination is used to project a pattern into the monitoring area, which is to provide a reliable structure independent of the scene. The goal is to generate so-called dense depth maps by various algorithms, i.e. depth maps containing a valid distance value possibly for all pixels. The respective algorithm tries to match the pattern in a window of the one image with the other image. This is generally possible. However, the disparity can sometimes not be determined, or not be determined with sufficient reliability. The reason for example is insufficient quality of the pattern element in the image, such as due to unfavorable remission properties of the respective surface in the scene so that there is a loss of pattern structures which are the prerequisite of the correlation. In these cases, the respective depth value cannot be determined, and there remains an image defect, a so-called gap in the depth map.
From a safety-technical point of view, gaps must be considered as possible objects or object components as a precaution. However, this can severely reduce the availability because safety-related shutdowns are triggered by gaps rather than real objects.
In safety-related applications, it is typically required that an object with certain minimum dimensions is reliably detected. This property is called detection capability. As long as a relatively coarse detection capability is used, the objects to be detected are larger than the gaps, and the simple approach of treating gaps like objects or parts of objects does not affect the availability. However, a coarse detection capability leads to large safety margins and thus makes cooperation of man and machine more difficult by large distances throughout the day. There is therefore a need for a finer detection capability which enables considerably smaller safety margins. If in turn improved object detection with a finer detection capability simply treats gaps as objects, the impact of misdetections on the availability becomes apparent because gaps can no longer be distinguished from objects by their size.
In EP 2 275 990 B1, there is a twofold evaluation, namely whether there are critical gaps or connected pixel areas without gaps which are respectively larger than the smallest object to be detected. This implies nothing else but treating gaps like objects, with the adverse effects on the availability as described.
EP 2 819 109 A1 discloses a 3D sensor which detects objects of a minimum size and larger in a detection field. In order to correctly take into account the projective geometry of a stereo camera, areas of the depth map are compared with suitably selected templates. Gaps are also treated like objects, so that the problem of more frequent unnecessary shutdowns at finer detection capability remains.